{"id":8139,"date":"2025-04-24T10:16:47","date_gmt":"2025-04-24T10:16:47","guid":{"rendered":"https:\/\/www.version1.com\/en-us\/?p=8139"},"modified":"2026-07-23T12:08:55","modified_gmt":"2026-07-23T12:08:55","slug":"manners-cost-nothing","status":"publish","type":"post","link":"https:\/\/www.version1.com\/en-us\/blog\/manners-cost-nothing\/","title":{"rendered":"Manners Cost Nothing?\u00a0\u00a0"},"content":{"rendered":"

The Hidden Price of Politeness in AI Prompts<\/h2>\n

We\u2019ve all grown up hearing that good manners are free. A simple \u201cplease\u201d or \u201cthank you\u201d doesn\u2019t cost anything – at least not in human conversation. But when you\u2019re speaking to a large language model like GPT-4, societal norms or niceties cease to be a currency. AI<\/a> doesn\u2019t interpret politeness; it processes it, and every word, no matter how well-intentioned, adds a little more computational work.<\/p>\n

Sam Altman, CEO of OpenAI, recently pointed out that these extra pleasantries whilst seemingly harmless are contributing to significant infrastructure costs. The figure he calculated? Tens of millions of dollars. It sounds dramatic, but the numbers hold up.<\/p>\n

So how expensive is a \u201cthank you\u201d, really? And does being polite to a machine still make sense in a world where every token has a price?<\/p>\n

Counting Tokens<\/h3>\n

Large language models operate on tokens, chunks of text that include parts of words or punctuation.<\/p>\n

\"$0.03<\/p>\n

Current GPT-4 charge example<\/i><\/p>\n

A word like \u201cplease\u201d might use one token. A slightly more padded phrase, like \u201cCan you please tell me the capital of France? Thank you,\u201d adds a few more tokens than a blunt prompt like \u201cCapital of France?\u201d<\/p>\n

On a per-prompt basis, the cost of those extra tokens is negligible. You\u2019re adding maybe $0.0001 to the bill; virtually nothing. But this isn\u2019t about a single user or a single prompt, it\u2019s about scale.<\/p>\n

OpenAI\u2019s systems process billions of queries each month. If even a small fraction of those includes niceties, the volume of additional tokens becomes significant. Add in the cost of output – responses like \u201cYou’re welcome\u201d or \u201cOf course, happy to help\u201d – and suddenly you\u2019re looking at hundreds of millions of extra tokens generated every day. That\u2019s where Altman\u2019s \u201ctens of millions\u201d figure starts to make sense.<\/p>\n

Energy, Not Just Money<\/h3>\n

The financial cost is only part of the story. Every token processed requires compute power, and compute power draws electricity. Estimates suggest that each ChatGPT prompt consumes around 2.9 watt-hours of energy. That\u2019s significantly more than a traditional search engine query, and the difference adds up fast.<\/p>\n

Including a few extra tokens in a prompt doesn\u2019t move the needle in isolation, but at scale these small increments accumulate. The more polite the interaction, the more the servers have to work, and the more electricity they consume.<\/p>\n

The carbon impact, too, is measurable. A single extra token isn\u2019t emitting much on its own, but millions of extra tokens processed daily across data centres around the world create a cumulative environmental footprint. The difference between terse prompts and wordier, more courteous ones may be minor in individual terms, but the aggregate impact is not insignificant.<\/p>\n

Different Models, Same Story<\/h3>\n

This isn\u2019t just an OpenAI issue. Every major AI platform – whether it\u2019s Claude, Gemini, DeepSeek, or Mistral – follows a similar model. Tokens in, tokens out. Compute costs, energy costs, and carbon emissions all tied to how much text the system needs to handle.<\/p>\n

Even if you\u2019re running models locally, on your own hardware, the dynamic doesn\u2019t change. You\u2019re still paying the price; just through your electricity bill rather than an API invoice. And if you\u2019re using a larger model like GPT-4, the power draw per token is higher than it would be on smaller, more efficient models like Claude Instant or Mistral 7B.<\/p>\n

The more complex the model, the more it costs – financially and energetically – to generate even basic replies. Politeness, in this context, means longer inputs and potentially longer outputs. That translates to more work for the machine.<\/p>\n

Do Manners Still Matter?<\/h3>\n

All of this raises a fair question: is it still worth being polite to an AI?<\/p>\n

There\u2019s a practical argument for yes. Prompt tone can influence the model\u2019s behaviour. Users who are more courteous in their prompts tend to receive responses that are more cooperative and balanced. From a product design perspective, that\u2019s not an accident, it\u2019s a feature; the experience feels smoother when interactions are framed positively.<\/p>\n

I put this to Filippo Sassi, Head of our AI Labs, here\u2019s how he put it:<\/p>\n

\n

\u201cWords like \u2018please\u2019 were part of the training data. The embeddings respond far better to them. When we experimented with stripping them out – treating them like stop words – the quality of the responses fell off a cliff.\u201d<\/p>\n

Filippo Sassi, Head of AI Labs, Version 1<\/cite><\/p><\/blockquote>\n

There\u2019s also the brand angle – OpenAI and other providers are incentivised to make AI feel natural, approachable, and human-like. That includes mirroring politeness, even if it adds a few extra microseconds of GPU time and a bit more server load.<\/p>\n

Altman himself has called this token overhead \u201cmoney well spent.\u201d And that\u2019s the trade-off: a slight cost increase in exchange for making interactions feel less transactional.<\/p>\n

Manners: Cheap, Not Free<\/h3>\n

You don\u2019t need to start issuing commands like a robot to save money or energy – but it\u2019s worth knowing that, these days, even a \u201cplease\u201d carries a price tag.<\/p>\n

It might be fractions of a cent, but whether you\u2019re paying for tokens or burning through compute on your own hardware, the cost lands somewhere. API users feel it directly, everyone else pays indirectly: through infrastructure strain, energy demand, and the emissions that come with it.<\/p>\n

So, does the old phrase still work? Not quite. Not in this context.<\/p>\n

Manners, it turns out, are still cheap. Just not entirely free. There\u2019s an old saying that friends and good manners will carry you where money won\u2019t go but, when it comes to interfacing with AI technology, we\u2019ll have to make very human value judgements on the costs.<\/p>\n

Nathan Marlor is our Head of Data and AI. For more detail on how our AI expertise is driving innovation and helping businesses around the world transform,\u202f click here<\/a>.<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"

The Hidden Price of Politeness in AI Prompts We\u2019ve all grown up hearing that good manners are free. A simple \u201cplease\u201d or \u201cthank you\u201d doesn\u2019t cost anything – at least not in human conversation. But when you\u2019re speaking to a large language model like GPT-4, societal norms or niceties cease to be a currency. AI […]<\/p>\n","protected":false},"author":12,"featured_media":8140,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"categories":[101],"tags":[115],"industry":[],"class_list":["post-8139","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","tag-ai"],"acf":[],"translations":[{"blog_id":1,"post_id":8139,"name":"Europe","code":"EN-GB","hreflang":"en-gb","url":"https:\/\/www.version1.com\/blog\/manners-cost-nothing\/","is_current":false},{"blog_id":13,"post_id":8139,"name":"Americas","code":"EN-US","hreflang":"en-us","url":"https:\/\/www.version1.com\/en-us\/blog\/manners-cost-nothing\/","is_current":true}],"yoast_head":"\nManners Cost Nothing?\u00a0\u00a0 | Version 1 (US)<\/title>\n<meta name=\"description\" content=\"Manners don't cost anything - at least not in human conversation. 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